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Ahmad Taher Azar

Researcher at Prince Sultan University

Publications -  458
Citations -  12351

Ahmad Taher Azar is an academic researcher from Prince Sultan University. The author has contributed to research in topics: Computer science & Control theory. The author has an hindex of 47, co-authored 389 publications receiving 8847 citations. Previous affiliations of Ahmad Taher Azar include Misr University for Science and Technology & Yahoo!.

Papers
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Book ChapterDOI

Rough Set Based Feature Selection for Egyptian Neonatal Jaundice

P. K. Nizar Banu, +4 more
TL;DR: Rough set based feature selection methods for early intervention and prevention of neurological dysfunction and kernicterus that are the major causes of neonatal jaundice are analyzed and demonstrate features selected by U-TRS-QR are highly accurate and will be helpful for physicians for early diagnosis.
Book ChapterDOI

PID Controller for 2-DOFs Twin Rotor MIMO System Tuned with Particle Swarm Optimization

Ahmad Taher Azar, +4 more
TL;DR: In this paper, the system modelling process is done using the common conventional mathematical model based on Euler-Lagrange method to obtain a robust controller for the 2-DOFs Twin rotor multi input multi output (MIMO) system.
Book ChapterDOI

Fractional-Order Control Scheme for Q-S Chaos Synchronization

Adel Ouannas, +3 more
TL;DR: A general control scheme is introduced that can be applied to wide classes of fractional chaotic and hyperchaotic systems and provides further contribution to the topic of Q-S synchronization between fractional-order systems with different dimensions.
Book ChapterDOI

Dynamics, Synchronization and Fractional Order Form of a Chaotic System With Infinite Equilibria

Viet-Thanh Pham, +6 more
TL;DR: In this paper, a three-dimensional chaotic system with an infinite number of equilibrium points is presented, and the fundamental properties of such a system are investigated by using equilibrium analysis, phase portraits, Poincare map, bifurcation diagram, and Lyapunov exponents.
Book ChapterDOI

Classifying Upper Limb Activities Using Deep Neural Networks

Hassan Ashraf Elkholy, +4 more
TL;DR: This paper presents a classification method using Inertial Measurement Unit in order to classify six human upper limb activities and has been validated by real experiments showing that ANN network gives the best performance.